Proceedings of the conference on Visualization '98
The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations
VL '96 Proceedings of the 1996 IEEE Symposium on Visual Languages
CartoDraw: A Fast Algorithm for Generating Contiguous Cartograms
IEEE Transactions on Visualization and Computer Graphics
PixelMaps: A New Visual Data Mining Approach for Analyzing Large Spatial Data Sets
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Give chance a chance: modeling density to enhance scatter plot quality through random data sampling
Information Visualization
A Visualization System for Space-Time and Multivariate Patterns (VIS-STAMP)
IEEE Transactions on Visualization and Computer Graphics
Spatial ordering and encoding for geographic data mining and visualization
Journal of Intelligent Information Systems
Visualization of Geo-spatial Point Sets via Global Shape Transformation and Local Pixel Placement
IEEE Transactions on Visualization and Computer Graphics
Exploratory spatio-temporal data mining and visualization
Journal of Visual Languages and Computing
Scalable 2-Pass Data Mining Technique for Large Scale Spatio-temporal Datasets
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
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The information revolution is creating and publishing vast data sets, such as records of business transactions, environmental statistics, and census demographics. In human versus application domains, this data is collected and indexed by geospatial location. The discovery of interesting patterns in such databases through spatial data mining is a key to turning this raw data into valuable information. Challenges arise because newly available geospatial data sets often have millions of records, or even more. New techniques are needed to cope with this scale. The Wide Area Layout Data Observer (Waldo) is a novel visual data mining system, based on PixelMaps, for analyzing large geospatial data sets. PixelMaps combine density-based distortion of map regions with local pixel repositioning to highlight clusters and avoid data loss from over plotting. To enhance data exploration, Waldo involves the human in cluster discovery.